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I was expecting more discussion of alternatives. For instance, in cases where deep neural networks aren't desirable or don't outperform classical approaches, I
by nilkn 9y ago
I was expecting more discussion of alternatives.
For instance, in cases where deep neural networks aren't desirable or don't outperform classical approaches, I'm a big fan of boosted decision trees, due to their accuracy on many real-world datasets, their ease-of-use, and the existence of great open source implementations. xgboost (which routinely wins Kaggle competitions) and Spark MLLib both have high-performance distributed training algorithms for gradient boosted trees. And as far as hyperparameter searches go, there just aren't as many parameters to optimize. (And frameworks like Spark are already fantastic for embarrassingly parallel tasks like hyperparameter searches.)
- nl 9y agoSpark MLLib both have high-performance distributed training algorithms for gradient boosted trees. Well it exists, but I wouldn't describe it as high performance in either accuracy or speed. I'm a big fan of Spark, but Spark ML needs some love from people who actually use it. Until that happens, just use XGB (which now has Spark integration[1]) [1] http://dmlc.ml/2016/03/14/xgboost4j-portable-distributed-xgboost-in-spark-flink-and-dataflow.html http://dmlc.ml/2016/03/14/xgboost4j-portable-distributed-xgb...